Researchers develop a more robust machine-vision architecture by studying how human vision responds to changing viewpoints of objects.

Stimuli that sound or look like gibberish to humans are indistinguishable from naturalistic stimuli to deep networks.

Model registers “surprise” when objects in a scene do something unexpected, which could be used to build smarter AI.

How people interpret musical notes depends on the types of music they have listened to, researchers find.

The DiCarlo lab finds that a recurrent architecture helps both artificial intelligence and our brains to better identify objects.

Computer model could improve human-machine interaction, provide insight into how children learn language.